Spatial Data Science by Pebesma, Edzer; Bivand, Roger;

Short description:

Spatial Data Science is a book for data scientists with intermediate R knowledge. The book gives a detailed explanation of the core spatial software packages for R: sf for simple feature access, and stars for raster and vector data cubes – array data with spatial and temporal dimensions.

Long description:

Spatial Data Science introduces fundamental aspects of spatial data that every data scientist should know before they start working with spatial data. These aspects include how geometries are represented, coordinate reference systems (projections, datums), the fact that the Earth is round and its consequences for analysis, and how attributes of geometries can relate to geometries. In the second part of the book, these concepts are illustrated with data science examples using the R language. In the third part, statistical modelling approaches are demonstrated using real world data examples. After reading this book, the reader will be well equipped to avoid a number of major spatial data analysis errors.

The book gives a detailed explanation of the core spatial software packages for R: sf for simple feature access, and stars for raster and vector data cubes – array data with spatial and temporal dimensions. It also shows how geometrical operations change when going from a flat space to the surface of a sphere, which is what sf and stars use when coordinates are not projected (degrees longitude/latitude). Separate chapters detail a variety of plotting approaches for spatial maps using R, and different ways of handling very large vector or raster (imagery) datasets, locally, in databases, or in the cloud. The data used and all code examples are freely available online from https://r-spatial.org/book/. The solutions to the exercises can be found here: https://edzer.github.io/sdsr_exercises/.

“I think that this is an important book. I am convinced it will be seen as a reference for scientists working with spatial data in R but also as a textbook for scientists and postgraduate students who are learning the concepts and how to do it practically in R (admittedly at a very advanced level!). It has certainly be on the shelf of everyone working with and teaching spatial data in R.”
-Hanna Meyer, Institute of Landscape Ecology, University of Münster, Germany

Table of Contents:

Part 1. Spatial Data  1. Getting Started  2. Coordinates  3. Geometries  4. Spherical Geometries  5. Attributes and Support  6. Data Cubes  Part 2. R for Spatial Data Science  7. Introduction to sf and stars  8. Plotting spatial data  9. Large data and cloud native  Part 3. Models for Spatial Data  10. Statistical modelling of spatial data  11. Point Pattern Analysis  12. Spatial Interpolation  13. Multivariate and Spatiotemporal Geostatistics  14. Proximity and Areal Data  15. Measures of spatial autocorrelation  16. Spatial Regression  17. Spatial econometrics models  Appendix A. Older R Spatial Packages  Appendix B. R basics